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Dimension Reduction Based on Orthogonality — A Decorrelation Method in ICA

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Artificial Neural Networks and Neural Information Processing — ICANN/ICONIP 2003 (ICANN 2003, ICONIP 2003)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2714))

Abstract

In independent component analysis problems, when we use a one-unit objective function to iteratively estimate several independent components, the uncorrelatedness between the independent components prevents them from converging to the same optimum. A simple and popular way of achieving decorrelation between recovered independent components is a deflation scheme based on a Gram-Schmidt-like decorrelation

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Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, The Chinese University of Hongkong, Hongkong

    Kun Zhang & Lai-Wan Chan

Authors
  1. Kun Zhang
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  2. Lai-Wan Chan
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Editor information

Editors and Affiliations

  1. Bogazici University, Bebek, 34342, Istanbul, Turkey

    Okyay Kaynak  & Ethem Alpaydin  & 

  2. Laboratory of Computer and Information Science, Helsinki University of Technology, P.O.B. 5400, 02015, Finland

    Erkki Oja

  3. Department of Computer Science and Engineering, The Chinese University of Hong Kong, Shatin, Hong Kong

    Lei Xu

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© 2003 Springer-Verlag Berlin Heidelberg

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Zhang, K., Chan, LW. (2003). Dimension Reduction Based on Orthogonality — A Decorrelation Method in ICA. In: Kaynak, O., Alpaydin, E., Oja, E., Xu, L. (eds) Artificial Neural Networks and Neural Information Processing — ICANN/ICONIP 2003. ICANN ICONIP 2003 2003. Lecture Notes in Computer Science, vol 2714. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44989-2_17

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  • DOI: https://doi.org/10.1007/3-540-44989-2_17

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-40408-8

  • Online ISBN: 978-3-540-44989-8

  • eBook Packages: Springer Book Archive

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